Plant Leaf Disease Classification Using Convolutional Neural Network Based on Digital Images

Raditya Danar Dana; Ahmad Faqih; Nisa Dienwati Nuris; Riri Narasati

doi:10.59934/jaiea.v5i2.2183

Authors

Raditya Danar Dana STMIK IKMI Cirebon
Ahmad Faqih STMIK IKMI Cirebon
Nisa Dienwati Nuris STMIK IKMI Cirebon
Riri Narasati STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v5i2.2183

Keywords:

Convolutional neural network, Image classification, Leaf disease, Plant disease detection, Streamlit application

Abstract

Monitoring plant health is an important factor in maintaining agricultural productivity. Manual identification of leaf diseases requires expert knowledge and is prone to errors due to visual similarities among disease symptoms. This study aims to develop a plant leaf disease classification system based on digital images using a Convolutional Neural Network (CNN) approach. The dataset consists of plant leaf images representing three disease classes: Corn–Common rust, Potato–Early blight, and Tomato–Bacterial spot. Prior to model training, the images undergo preprocessing steps including image resizing and pixel normalization. The performance of the CNN model is evaluated using a testing dataset that is not involved in the training process, employing accuracy, confusion matrix, precision, recall, and F1-score as evaluation metrics. Experimental results show that the proposed model achieves a test accuracy of 95.56%, with balanced performance across all disease classes. In addition to quantitative evaluation, the trained model is implemented in a Streamlit-based application, allowing users to upload plant leaf images and obtain disease classification results interactively. The findings indicate that the CNN-based approach is effective for plant leaf disease classification and has potential application as an early decision-support system for plant health monitoring.

Downloads

Download data is not yet available.

References

L. Li, S. Zhang, and B. Wang, “Plant disease detection and classification by deep learning: A review,” IEEE Access, vol. 9, pp. 56683–56698, 2021, doi: 10.1109/ACCESS.2021.3069646.

H. Guan, C. Fu, G. Zhang, K. Li, P. Wang, and Z. Zhu, “A lightweight model for efficient identification of plant diseases and pests based on deep learning,” Front. Plant Sci., vol. 14, 2023, doi: 10.3389/fpls.2023.1227011.

S. Quan, J. Wang, Z. Jia, et al., “MS-Net: A novel lightweight and precise model for plant disease identification,” Front. Plant Sci., vol. 14, 2023, doi: 10.3389/fpls.2023.1276728.

X. Zhang, J. Li, Y. Liu, et al., “Plant pest and disease lightweight identification model by fusing tensor features and knowledge distillation,” Front. Plant Sci., 2024, doi: 10.3389/fpls.2024.1443815.

S. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Comput. Electron. Agric., vol. 145, pp. 311–318, 2021, doi: 10.1016/j.compag.2018.01.009

M. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,” Comput. Electron. Agric., vol. 161, pp. 272–279, 2021, doi: 10.1016/j.compag.2019.03.032.

A. Saleem, M. Akhtar, M. Sharif, and M. Raza, “Automated diagnosis of plant diseases using deep learning,” Appl. Sci., vol. 11, no. 9, 2021, doi: 10.3390/app11094168.

A. Dhaka, S. Jain, and R. Sinhal, “A deep learning-based approach for plant disease classification using leaf images,” IEEE Access, vol. 9, pp. 125978–125989, 2021, doi: 10.1109/ACCESS.2021.3111250.

Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2021, doi: 10.1038/nature14539.

J. Wang, C. Li, and Y. Liu, “Lightweight convolutional neural network for plant disease classification,” IEEE Access, vol. 10, pp. 11456–11466, 2022, doi: 10.1109/ACCESS.2022.3143210.

R. Sharma and P. Patel, “CNN-based plant disease classification using leaf images,” J. Plant Dis. Prot., vol. 129, no. 4, pp. 789–799, 2022, doi: 10.1007/s41348-022-00579-6.

X. Chen, H. Zhang, and Y. Li, “Efficient CNN architectures for plant disease identification,” Front. Plant Sci., vol. 14, 2023, doi: 10.3389/fpls.2023.1182456.

S. Ahmad, A. Ullah, and M. Khan, “Challenges and limitations in deep learning-based plant disease detection,” Sensors, vol. 23, no. 4, 2023, doi: 10.3390/s23042156.

Z. Liu, Y. Zhang, and H. Wang, “Performance evaluation of CNN models for plant leaf disease classification,” IEEE Access, vol. 12, pp. 33421–33432, 2024, doi: 10.1109/ACCESS.2024.3367219.

S. Mishra, R. Sachan, and P. Rajpal, “A systematic workflow for deep learning-based plant disease classification using leaf images,” IEEE Access, vol. 10, pp. 103421–103432, 2022, doi: 10.1109/ACCESS.2022.3198745.

A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Comput. Electron. Agric., vol. 147, pp. 70–90, 2021, doi: 10.1016/j.compag.2018.02.016.

D. P. Hughes and M. A. Salathé, “An open access repository of images on plant health to enable the development of mobile disease diagnostics,” arXiv:1511.08060, 2015. [Online]. Available: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset

A. B. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Comput. Electron. Agric., vol. 195, 2022, doi: 10.1016/j.compag.2022.106843.

M. A. Hasan, M. J. Islam, and J.-M. Kim, “A deep learning approach for plant disease detection using leaf images,” Sensors, vol. 21, no. 14, 2021, doi: 10.3390/s21144694.

S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, “Deep neural networks based recognition of plant diseases by leaf image classification,” Comput. Intell. Neurosci., 2020, doi: 10.1155/2020/3289801.

S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” Proc. Int. Conf. Eng. Technol. (ICET), 2020, doi: 10.1109/ICEngTechnol.2017.8308186

M. Grandini, E. Bagli, and G. Visani, “Metrics for multi-class classification: an overview,” Pattern Recognit. Lett., vol. 129, pp. 64–74, 2020, doi: 10.1016/j.patrec.2019.11.005

T. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf. Process. Manag., vol. 58, no. 4, 2021, doi: 10.1016/j.ipm.2021.102682

A. B. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Comput. Electron. Agric., vol. 195, 2022, doi: 10.1016/j.compag.2022.106843.

J. Brownlee, “A gentle introduction to overfitting and underfitting in machine learning,” Machine Learning Mastery, 2020, doi: 10.1016/j.eswa.2020.113483